Mental Health
Ψυχική Υγεία
Peace & NGOs
Ειρήνη - ΜΚΟ
Publications
Βιβλιογραφία
CV - Resume
Βιογραφικό
Thesis for the degree of Master of Science in Psychiatric Theory and Research Methods
by Dr.
Michael Moutoussis fzsemmo@gn.apc.org
at the Department of Psychiatry, University
College London Medical School, Wolfson Building, Middelesex Hospital,
London W1 8AA ,
Supervised by Dr. Martin Orrell,
Dept. of Psychiatry and Dr. George Houghton, Dept. of
Psychology
I. Abstract
Introduction
: In Alzheimer's disease (AD) executive and working memory deficits
often compromise the safety and independence of patients. Such executive
functions are understood to organise working memory. The latter has been
described as consisting of "slave" systems co-ordinated by a Central Executive
System (CES) that controls attention. The CES is particularly affected
in AD. Computer models have contributed greatly to the understanding of
selective attention, but not with reference to AD. We explore the potential
for an understanding of the loss of executive function in AD using computer
modelling and we review recent research from a variety of fields whose
integration is central to this approach. Research has suggested that high
educational achievement protects against the development of AD, possibly
by helping preserve such executive functions. This finding is explored
in the light of the modelling studies. Executive function in AD has been
investigated experimentally, most notably by the group of Baddeley and
colleagues. Dual task performance in normal elderly controls has been compared
with that of AD patients. In the AD group, performance in any task was
reduced by the presence of a concurrent task. With the passage of time
the effect of any concurrent task on any primary one increased for the
AD group but not for the controls. This demonstrated a specific executive
deficit in the patient group.
Aims
: It is feasible to develop rigorous models of 'central executive'
function. The main hypothesis of our study is that the application of the
computational model of attentional control of Houghton and co-workers could
account for the pattern of deficit observed in AD patients.
Methods
: Dual task performance data was simulated by using models of attentional
control derived from those of Houghton and co-workers. In these models
for each sensory modality bottom-up and top-down signals are compared in
'match-mismatch' modules. The output of these modules can excite or inhibit
lower level sensory modules towards particular input features. Two attentional
control systems, one for each modality, were combined in the present simulations
to account for dual task performance. Sensory and 'match-mismatch' modules
were linked by cross modality inhibitory attentional control whose implementation
here was guided by the neuropathology of early AD. Key building blocks
of the original Houghton model were analysed mathematically to explain
the behaviour of the overall models. Alternative building blocks and structural
modifications were explored.
Results :
1. The models adequately simulated performance of controls.
2. Reduction in single task performance in AD was also successfully simulated.
3. Reduction in dual relative to single task performance in AD was only simulated for narrow parameter ranges (was not robust).
4. Model capacity for gradual performance deterioration was limited.
5. Positive feedback loops involving units such as those used by Houghton have drawbacks such as spurious activated states and limited capacity for increases in gain.
6. These problems can be overcome by using units with a flatter response at their rest state.
7. Incorporation of direct lateral inhibition at the level of sensory cortex greatly improves model performance.
8.
Preliminary implementation of the matching mechanism as synchronisation
in the oscillatory activities of two brain areas and of lateral inhibition
as oscillatory noise interference between modalities is successful in qualitatively
simulating single and dual task performance for normal controls and for
sufferers of Alzheimer's disease.
Discussion
: The models described here were successful in simulating many features
of attentional control. However, they do not robustly predict the experimental
results without substantial modification. When Houghton's theory is reviewed
most of the conceptual essentials are retained, but many important details
of implementation are rejected. More physiological details of neural masses
help improve models. It is argued that direct lateral inhibition is unlikely
to be a biological default between modalities: its essential point is likely
to be the presence of pathway interference at specific levels (rather than
across levels) which is preserved or even augmented in AD. In the light
of recent neuroscientific findings, a modified theory is suggested that
postulates 'Match / mismatch' function to involve oscillatory synchronisation.
Interference within levels (similar to direct lateral inhibition) is hypothesised
to impair synchronisation between levels. Preliminary simulations support
this theory but more extensive exploration is required. It is envisaged
that in the future the study of executive function may aid the assessment
of the ability for independent living of patients and the understanding
of the protective role of education in AD.
1.
The importance of executive functions in Alzheimer's disease
Dementia
is one of the most frightening and costly diseases to affect humanity,
one that eventually destroys most mental abilities. Memory deficits are
prominent but not necessarily sufficient to impose severe restrictions
in the patient's lifestyle and independence. When, however, executive as
well as mnemonic functions are affected, so that the patient cannot carry
out the schemas necessary for daily activities, the patient puts herself
in danger. An example would be having difficulty coordinating the sequence
of actions required to make a cup of tea. It is therefore of great practical
importance to understand and support those mental mechanisms concerned
with executive and attentional functions.
Research
into Alzheimer's disease (AD), the commonest cause of dementia, has therefore
explored in recent years the 'executive' deficits (Baddeley et al, 1991).
The term `executive' is used in different ways by different researchers
and in different contexts, in line with the ignorance characterising new
fields of enquiry. Jaak Panksepp (1998) defines: " 'Executive system' implies
that a neural system has a superordinate role in a cascade of hierarchical
controls ". 'Executive' is applied to planning, sequencing, control of
attention and of working memory and specific functions such as cognitive
set shifting or suppression of dominant tendencies.
2.
Psychological theories of executive control of attention
Specific
functional modules, such as the Supervisory Attentional System or the related
Central Executive System (CES) have been proposed to malfunction in AD
in order to account for executive deficits (Baddeley 1996). According to
such psychological models a 'Supervisory Attentional System' is hypothesised
to co-ordinate the lower level, or 'slave', components of working memory,
i.e. the 'Phonological Loop' and the 'Visuospatial Scratchpad'. Since Baddeley's
pioneering work there has been active debate as to the modularity and brain
localisation, of functional modules that subserve and co-ordinate working
memory (Wickelgren, 1997).
3.
Dual Attention and the Central Executive System
Baddeley
and colleagues (1991) examined executive function in a series of experiments
which form the basis for the present analysis. They attempted to test whether
AD disproportionately affects Central Executive allocation of attention,
so that dual task performance is affected more than the constituent tasks.
They compared patients with mild AD with normal elderly controls and followed
them up over one year. In these experiments a primary tracking task, following
a randomly moving white square, was combined with graded secondary tasks.
The difficulty of the tracking task was first individually adjusted in
the absence of secondary tasks so that subjects managed to stay on the
square for 40-60% of the time. The difficulty was then fixed. The simplest
concurrent secondary task was 'articulatory suppression': the subject counted
repeatedly from 1 to 5 during pursuit tracking. The next stage was reaction
time to tones : the subject had to press a foot switch as soon as an auditory
stimulus was presented. Reaction times and percentage of missed tones were
recorded. The final stage was a memory span task. In this the maximum length
of a random digit sequence that the subject could reliably repeat back
was determined in the absence of pursuit tracking prior to each of the
three testing sessions. Sequences of this 'subject-tailored' maximum length
were then presented during pursuit tracking and recall performance recorded.
The
results obtained were consistent with the hypothesis. In the AD group,
performance in any task was reduced by the presence of a concurrent task.
A key result was that with the passage of time the effect of any concurrent
task on any primary one increased for the AD group but not for the controls,
supporting the hypothesis of a specific executive deficit in the patient
group.
4.
Functions preserved in AD - Implications for Attention and Planning
In
a series of experiments complementing those of Baddeley, Simone & Baylis
(1997) showed that executive function was impaired in AD using a paradigm
involving suppression of a dominant response tendency. Subjects had to
quickly respond to a green light, but ignore a yellow one. AD subjects
showed many false positive responses, consistent with weak executive control.
To test for the cognitive level at which errors occurred, subjects were
asked how sure they were that they responded correctly. AD patients were
aware of their erroneous choices. The authors concluded that the executive
deficit did not involve early information processing but rather the efficient
implementation of a response.
5.
Dementia, education and executive function
Recent
epidemiological findings in dementia are related to the above concerns.
Low
educational achievement appears to be an independent risk factor for the
development of dementia (Orrell & Sahakian, 1995). The aged of lower
educational achievement more commonly fail tests of activities of daily
living (Zhang et al, 1990), while the better educated require greater damage
to cortical areas important for executive function to get as impaired (Alexander
et al, 1997). The mechanisms underlying the protective effect of education
are however poorly understood. Mathematical modelling could help clarify
relevant hypotheses.
6.
Mathematical Modelling in Psychiatry: some difficulties
In
most sciences modelling has an important role between experimental studies
and theoretical analysis. It is not, however, obvious that mathematical
modelling has much place in the neurosciences in general and in psychiatry
in particular. Psychiatric theories are often conceived in qualitative
terms and expected predictions are derived on the basis of semantic inference
and common sense. However, many theories are not precise enough (they may
be underspecified) to derive accurate predictions from; or the system involved
may be too complicated for one to derive predictions by common sense (the
theory may be intractable).
A
rigorously specified model has many advantages : First, it forces a more
complete description of the problem. The key variables have to be defined
and theoretical assumptions become explicit. Second, explicit alternative
explanations of the data on which the model is based can be formulated.
Third, detailed predictions can be made: therefore falsification of a rigorously
defined model is easier i.e. the theory provides better means for its own
falsification (Notturno, 1984). Fourth, counterintuitive predictions of
the theory may become apparent. These may explain already existing data
difficult to understand on the grounds of common sense. It may also be
rigorously tested whether data seemingly contradicting the theory could
in fact be compatible with it. Fifth, ignorance about the theory can be
quantified, e.g. in terms of model parametres. Results of further experiments
can be anticipated and such experiments planned. Finally, once successful
models are developed, numerical experiments can be performed that would
be too difficult to carry out in vivo.
Although mathematical modelling is thus indispensable in the hard sciences, it has not fared as well in psychiatry. There are several reasons for this. Biological systems are so complicated that people think that a lot more needs to be known about them before meaningful modelling can take place. It is often unclear that biological systems have laws possessing explanatory power independent of the system's fine structure. It is also unclear how components and their interactions at any particular level of description give rise to collective properties in a non-trivial manner. Indeed, the theory of evolution favours a top-down, 'how is this feature teleologically reasonable', view over the reductionist, bottom up modelling approach. In addition it is sometimes possible to construct several different models that explain the data. These models may be difficult to tell from each other by experiment or may be poor at making novel predictions.
Mathematical modelling is culturally alien to psychiatrists; most importantly the questions that could be investigated by modelling simply do not occur to us. Our heuristics of science do not include the values of rigour, unification of ideas, economical description of phenomena and beauty of mathematical structure that guide the hard sciences.
Finally,
biological phenomena usually involve self-organising structures that consume
energy. In such systems measured variables do not change in proportion
each other, i.e. the systems are non-linear (Nicolis, 1991; Nicolis, 1989;
Kelso, 1995). The theory of nonlinear systems has only recently emerged
and its neurobiological applications are still at an early stage.
7. Modelling efforts in Alzheimer's disease
Despite
these obstacles, considerable inroads have been made in the modelling of
AD. Connectionism has aided the understanding of psychiatric disorders
and of AD in particular. A connectionist model consists of interconnected
units representing neurones or groups of neurones. Units can have simple
internal structure yet their networks can perform complex functions, made
possible by appropriate patterns of synaptic interconnections (Jeffery
& Reid, 1997). This is an anatomically inspired approach that can be
used to model brain function at the level of groups of cells (Traub et
al, 1997) or of individual cortical areas (Freeman, Yao & Burke, 1988).
The role of neurotransmitters can be elucidated. Models can be used to
investigate the transition from the level of nerve activities to that of
objects of perception and action (Freeman, 1991). Contextual meaning and
hence emotion can be taken into account (Armony et al, 1995). At the 'highest'
(symbol manipulation) level connectionist models can be used to simulate
objects of cognition such as memories (Hartley & Houghton, 1996). At
the neurotransmitter level they can help explain observed psychopathology
(Cohen & Servan-Schreiber, 1992).
The
first goal of modelling in AD has been the understanding of dysmnesia.
Pioneering studies (Carrie, 1993) used highly abstract connectionist models.
These explained the distributed storage of memories, their initial resistance
in the face of gradual neuronal loss (simulated by unit deletion in the
network) and their subsequent smooth decline. The use of fairly realistic
learning algorithms allowed important shortcomings of the model to be identified.
The greater impairment in new learning relative to memories laid before
'atrophy' could not be explained, and gradual deletion of neurones from
biologically more realistic networks failed to produce a gradual decrease
in recall. Performance remained intact until a large proportion of network
elements were lost, then dropped catastrophically. To simulate the gradual
course of the illness it was necessary to introduce synaptic compensation
as found in the real brain (Ruppin & Reggia, 1995). Synaptic compensation
is a biological constraint which permits the models to explain the gradual
degradation of recall performance, the differential sparing of remote memories
and the increased rate of false positive retrieval errors found in AD.
In
contrast to these sophisticated studies of memory little attention has
been paid to the modelling of executive function, despite its clinical
importance. Modelling loss of synapses might explain weakened executive
control while synaptic compensation might help explain the excess of false
positive responses, accounting for the findings of both Baddeley and Tipper
discussed above.
8. Mathematical models of Attentional Control
The
starting point for the present simulations is the theory of attentional
control of Houghton and colleagues (Houghton & Tipper 1994; Houghton
1995). In these models each sensory modality consists of low level sensory
modules, high level modules where the behavioural goals or targets of the
organism are stored and intermediate, 'match-mismatch', modules that compare
percepts with targets (fig 1a).
Figure
1b. Connectivity that implements the Attentional Control mechanism.
Filled circles are inhibitory synapses, arrowheads excitatory (as in following
figures). The Target field units feed to the corresponding Match/Mismatch
unit pairs. For each target feature (see 7) there is a unit pair (6) calculating
whether the feature is present, on the basis of input by the corresponding
Property unit (e.g. unit d gives bottom up input 5). If a match is present
the corresponding Match unit activates the On unit of the Property unit
coding the same feature (1) and inhibits its sister Off unit (2), thus
increasing the responsiveness of the Property unit. If a mismatch is detected
the Mismatch unit becomes active and reduces responsiveness (3, 4). Negative
feedback tends to reduce the activation of Property units subject to mismatch.
Coactivated features then cooperate to dominate perception and activate
an appropriate response. If for example property units a and b belong to
the same object, and unit a is activated, it tends to facilitate activation
of b both directly (12) and indirectly (10,11). Finally an emergent assembly
of features, signifying an object, activates a response schema.
The
output of the 'match-mismatch' modules can excite or inhibit the lower
level sensory modules towards particular input features, and thus attend
or ignore these particular features: this is the crucial mechanism of attentional
control in the model. The activated features are then combined into attended
percepts.
The
models of Houghton and co-workers can simulate a large number of experimental
data on selective attention. These include perceptual distracter processing,
negative priming, response binding in the presence of distractors and inhibition
of return. The models are built around important principles such as attentional
control of perceptual gain regulation, opponent processing and competitive-cooperative
intra-module interactions. Drawbacks of the model include first, its loose
basis on physiology. This is most evident in the match / mismatch module.
Its units perform logical computations whose physiological implementation
is quite unclear. Secondly, the whole model is constructed on the basis
of information-processing constraints. This is no bad thing in itself but
it ignores how dynamical constraints can give rise to structure. Levine,
Parks & Prueitt (1993) in their review of the methodology of simulation
of `frontal' cognitive functions expect that functionally and structurally
distinct levels of brain activity are separated by underlying dynamical
constraints. Thirdly, the Houghton model is over-stable. Once perceptual
input is terminated object assemblies tend to persist. This necessitates
the introduction of decaying synaptic weights between units of the Object
field (connections (9) and (10) in fig. 1b). Target node activities also
have to be made to decay. Freeman (1992) has discussed how positive and
negative feedback loops in neural systems need not convey excessive stability
if allowed to operate within oscillatory regimes.
Context
- dependent allocation of attention has been studied by Cohen and coworkers
(1992) using a model of the Stroop task. In this task subjects are presented
with dual stimuli, e.g. the name of a colour spelt out and the ink colour
in which the word is written. They are asked to either read what the word
says or to name its ink colour as quickly as possible. Each 'modality'
(reading vs. colour naming) has its own 'pathway' in the model. Both are
influenced by a 'task demand' module which the authors identify with a
function of the prefrontal cortex. Cohen finds that 'attentional selection
can be thought of as the mediating effects that the internal representation
of context [here, of the instruction the subject has received from the
experimenter] has on processing' (fig. 2).
Compared
to the theory of Houghton and coworkers, the model of Cohen et al includes
a simpler 'Matching' mechanism (another way of looking at the function
of the hidden units !) capturing essential biological plausibility. It
doesn't, however, include any cross-modality interaction except at the
output level; Its simplest modification to conform to the dual task layout
would therefore completely decouple modalities. It could therefore not
predict dual-task interference effects. An important finding from the work
of Cohen and coworkers is that 'the degree to which a process relies on
attention is determined by the strength of the underlying pathway'. This
may mean that a disproportionate attentional deficit may be not because
a 'Central Executive' is particularly affected in AD but because eroded
underlying pathways would take stronger attentional modulation to perform.
Figure 2a. Modular structure of the model of attention in the Stroop task according to Cohen and coworkers. External input is received by the input units, C ; In order for it to activate the output units, D, activity passes through the intermediate, 'hidden' units B. These are in part activated by units A which express the context to which the organism must give priority. These are similar to the 'target' units of the Houghton models (fig. 1) in that they are independently and externally activated.
Figure 2b. Detailed structure of Cohen's Stroop task model. All the synapses in or by the 'reading pathway' are shown. The connections of the colour naming pathway are similar but weaker. Note the absence of cross modality inhibition.
Figure
2c. The 'hidden' units of the Cohen model perform a graded AND (or
'match') function by becoming activated, and therefore allowing signal
to propagate along their respective pathways, when not only bottom-up excitation
is present from the stimulus but also descending facilitation.
9. Cerebral Localisation of Supervisory Attentional / Central Executive processes
The
concurrent tasks involved in the Baddeley experiments were chosen so as
to minimise competition for local resources and thereby to highlight the
possibly shared 'Central Executive' requirements. Thus the primary task
- tracking - is presumed to involve the 'visuospatial scratchpad' slave
system while the secondary the 'phonological loop' for example. Of course
both are shown to depend on attention 'allocated' to them by the presumed
central executive. While earlier imaging studies showed little overlap
between the brain areas activated by verbal and non verbal working memory
tasks (Raichle, 1993), more recent studies have concluded that the dorsolateral
prefrontal cortex contributes to the maintenance of both verbal and nonverbal
information (Fiez et al, 1996). Recent imaging studies indicate that rCBF
decreases in sensory areas that are not to be attended (Drevets et al,
1995), in agreement with theoretical studies that place emphasis on inhibitory
mechanisms in attention.
The
deterioration of the cholinergic system in AD is thought to affect attention
significantly. Sahakian has demonstrated that when cholinergic function
enhancing drugs improve neuropsychological performance in AD this is attributable
more to an improvement of attentional rather than memory function (Lawrence
& Sahakian, 1995).Imaging studies indicate that Scopolamine, a cholinergic
antagonist, attenuates memory-task-induced increases of rCBF in the right
anterior cingulate but also bilaterally in the prefrontal cortex.
Taken
together these findings support a model of AD where elements corresponding
to particular aspects of prefrontal function are either damaged or functionally
dysconnected (Schreiter-Gasser et al, 1993; Morris, 1994) to the rest of
the model. The primary candidates for this, in Houghton's terms, would
be the 'target field' and the 'match-mismatch' (fig. 1a). Damage to either
would result in reduced attentional control on lower-level units, a priori
equally impairing excitatory and inhibitory control.
11.
Physiological basis for modelling
In
most connectionist psychological models units are very simplified in biological
terms, being derived more on the basis of constraints from psychology.
This does not by itself imply that the functional approximation is crude,
as biological systems are self-organising, far from equilibrium and hence
their emergent properties at any one level may be robust within a functionally
important range of conditions. However the adequacy of the approximation
is always a matter of concern.
Numerous
abstract models are used to provide tractable equations for modelling neuronal
elements. Often used is the (logistic) sigmoid neuron where output is a
sigmoid function of the sum of all inputs to the neuronal soma within a
preceding short time interval. Continuous time models usually include a
differential operator acting on neuronal state variables equated with a
sigmoid function of inputs, which may involve delays. The prototype is
the model of Wilson & Cowan (1973). The units used in the work of Houghton
et al involve first order operators and no delays. More physiological alternatives
range from the second-order-operator models of neuronal populations of
Freeman (1975) to simulations of large number of neurons with details of
their ionic currents and other biophysical properties (Traub et al, 1997).
I shall consider the logistic sigmoid neuron as a minimum requirement for
physiological relevance and more complicated models when detailed time
evolution of the system is simulated.
12.
Dynamical analysis and biological models
It
is important to analyse the performance of the neural systems involved
in attention not just in terms of facilitation and inhibition of units,
but in terms of attractor dynamics. This involves considering what patterns
of activity are open to the network, what is the stability of such patterns
and how the 'landscape' (phase space) of all such available patterns changes
under different conditions. We may, for example, think that a stable performance
of the primary task corresponds to an attractor set of the entire network
but one with specific directions of relative instability. Perturbations
along such directions (corresponding, for example, to the auditory signal)
can 'flip' the system into another, possibly transient, 'attractor' set
(corresponding to auditory perception, match and output). The motivation
for thinking of this model in terms of attractors and their stability comes
from several sources.
First,
the model of Houghton et al is, as discussed, over-stable. Its authors
thus introduced modifications with little psychological or physiological
support. Dynamical analysis of the model's stability could, alternatively,
clarify the causes and solutions to the problem in a less ad hoc way.
Second,
while the attractors that are used in most psychological models are point
attractors, the ones found experimentally in investigations of thalamocortical
interaction and those extensively studied in limbic structures (e.g. entorrhinal
cortex, heavily involved in AD) are par excellence oscillatory (Kay, 1996;
Gray, 1994). There is evidence for thalamic and limbic areas being involved
in attention (Forstl & Sahakian, 1993) and particularly in the matching
process. Freeman and co-workers have demonstrated dramatically the effect
that attention and behavioural significance have on the patterns of oscillatory
activation of the olfactory cortex (Grajski & Freeman, 1989, Eeckman
& Freeman, 1991, Kay,1996). An important component of the binding of
sensory features into visual percepts also relies on oscillatory attractors
(Gray, 1994; Bressler, 1996). In the present models oscillations would
arise naturally if realistic synaptic delays were incorporated in the existing
negative feedback loops. Superposition of oscillatory attractors, switching
between such attractors and global modulation of oscillatory cortical activity
differs from equilibrium (point attractor) dynamics as applied to the same
brain functions. It is thus important to consider our present models in
terms of attractor dynamics to prepare the ground for incorporation of
the above findings. Introducing oscillatory dynamics must however be necessitated
by both the probable applicability of the physiological findings and the
need to use that level of description to overcome limitations of the information
processing approach used so far.
Another
reason to consider analysis in terms of attractors is illustrated by the
dynamical analysis of errors in a neural network model of dyslexia. This
has demonstrated that dynamical analysis can explain some counterintuitive
experimental findings that are replicated by neural network models (Hinton,
Plaut & Shallice, 1993). Burgess and Hitch (1996) claim that a model
that replicates experiment but "cannot be mapped onto a conceptual understanding
of the processes giving rise to behaviour is useless". The dyslexia model
demonstrated that this conceptual understanding, and therefore the usefulness
of such a model, may well depend on the understanding of the attractors
involved.
1.
To develop the theory of normal 'central executive' function.
Attentional
control may depend on the matching between sensory representations of perceived
objects and partial, 'target 'representations of sought objects (Houghton
& Tipper, 1994). The central hypothesis of the present work is that
models of this matching process can simulate executive attentional control
during dual tasks.
I
aimed to simulate the influence of attention in normal subjects during
single- and dual- task conditions by bringing together previously developed
models of attentional control ( Houghton & Tipper,1994) with the dual
pathway concept of Cohen and co-workers (1992) .
Baddeley's
reaction time experiment could be simulated with a model of attentional
control with modality specific pathways (fig. 3) . This will be used as
the nucleus of the present study as it includes all the important experimental
findings. Once this is adequately modelled, other dual task experiments
could be simulated as further tests of the main hypothesis.
2.
To simulate dual task performance deficits demonstrated in AD.
By
introducing neuropsychologically plausible 'lesions' in the above model
I aim to replicate the experimental findings in mildly demented subjects.
AD lesions can be simulated as an impairment of the match / mismatch field.
The modules situated closer to the sensory and motor interfaces will in
this approximation be treated as intact, in line with the localisation
of such modules within sensory cortices, preserved in early DAT.
As
in the Baddeley experiments, a greater attentional control deficit (corresponding
to more advanced AD) should lead to increasing divergence between single-task
and dual-task performance. I aim to show that progressive 'damage' would
suffice to account for the deterioration of DAT subjects over time.
'Damage'
to the model proposed here does not simulate the AD effects in a trivial
manner. Reducing the efficiency of cross modality inhibition might decouple
modalities, and dual task performance may improve relative to single task
performance. This possibility makes the model proposed more falsifiable.
3.
To identify important limitations of the proposed models and to suggest
ways to overcome them.
When model components or architecture are found to limit ability to simulate experimental results, I aim to improve on these not only by introducing the minimum sufficient modifications but also by guiding such modifications by appropriate physiological considerations.
1. Methods used for Systematic Review of literature Not available online - Please e-mail me for details
2. Criteria for setting model structure Not available online - Please e-mail me for details
3.
Effective use of programming environments
Not available online
- Please e-mail me for details
4.
Core model structure and programming
A
system of interconnectable modules (that can represent neurons or other
connectionist 'units' such as lumped cortical areas) with their connections
('dendritic trees'), outputs ('axons') and state variables, was developed
(Appendix I). These 'units' could be driven by arbitrary dynamics to specify
how they respond to their inputs so as to produce outputs. Classes were
programmed to handle time-dependent equations to drive the 'units'. Classes
used for solving systems of ordinary differential equations (ODEs) were
based on an adaptive step, fourth order Runge-Kutta method (Press et al,
1988). Classes for return maps were also programmed.
Modelling
emulated the progression from single task to dual task experiments and
examined the effects of attention and concurrent task on performance. The
evolving structure permitted exploration of the simplest models to help
determine and the most plausible characteristics of subsequent ones.
As
the Baddeley experiments that I aimed to simulate do not report detailed
discrimination of features within each sensory system (e.g. detection of
a tone, rather than discrimination between tones, is used), each cortical
area was simulated by a 'lumped' unit. Apart from this being common practice,
both psychological (Houghton's work) and physiological (Freeman, 1992)
demonstrations have been provided of the validity of treating a cortical
area as a 'lumped' neural mass for the purposed of simulation of aspects
of its overall behaviour, such as its overall activation. The cortical
area corresponding to each of the two sensory modalities in a dual task
was initially simulated by an 'attentional triad' consisting of one 'perceptual'
and two 'gain control' units (fig. 4; cf. fig. 2a field C).
Figure 4: Models directly derived from the attentional control theory of Houghton & coworkers . Models use 'attentional triads' (gain units labelled + and -) as lumped representations of perceptual cortices. a. Direct lateral inhibition; also synapse and cortical area labels, which also apply to (b.) and (c.) b. Descending control of attention only. c. Descending and ascending connections forming a 'match' mechanism.
1.
Direct Lateral Inhibition only. It is thought (Nunez, 1995) that most inter-area
corticocortical connections are excitatory. Within the Houghton model a
lateral inhibitory effect is however easily implemented through excitation
by one area of the inhibitory gain units of the other (fig. 4a).
2.
Descending attentional control. Descending influence from a task-specific
attentional area provides excitatory input to its 'own' and inhibitory
input to the 'opposite' modality (fig. 4b), according to the principle
of cross-modality inhibitory attentional control.
3.
Descending and ascending projections implement 'match' loops. As there
were no discrimination tasks involved, the necessity to consider 'mismatch'
loops did not arise. However, the issue of the details of implementation
the 'match' units did arise. Rather than the units being programmed to
directly perform logical functions, as in the original Houghton models,
the functionally approximate but physiologically more plausible use of
a simple logistic sigmoid neurons, such as in fig. 2, was adopted. Parameters
for these are not available either from physiology or previous work. They
were therefore initially set to be such that for the simulation of normals
attending to a single task, the presentation of the stimulus changed 'match'
unit activation from a low (~10% of max), resting state to a high (~90%
max), activated state. For AD, the matching mechanism was impaired; there
was no a priori reason to suggest that such impairment should affect the
excitatory output of the mechanism more than its inhibitory output. AD
was therefore assumed not to affect their balance but only diminish their
output.
Alternative
explorations first examined the effect of DLI coexisting with MAS (fig.
5) and simulating attentional triads consisting of units more representative
of the physiological properties of cortex.
Figure
6a: Oscillatory model of a 'match' mechanism; Adaptation of the dual
pathway model of fig 3. to include oscillatory activity. Cross-modality
interference is now not associated with descending control but with local
effects, as per interpretation of the lateral inhibition findings. This
is shown by the asymmetric influence of the 'match' modules of each pathway
on each other (grey arrows)
d2x(i)/dt2 + A*dx(i)/dt + B*x(i)
=
S(j) {I(j)} + S(n) { Kni*Q(n)[x(n)] }, Relation
(Rln) 1
Problematic
simulation results were investigated both analytically and graphically,
using dynamical systems methods (Abraham & Shaw, 1992, Devaney, 1989).
The utilisation of reduced systems of return maps to investigate time-dependent
behaviour of biological systems was inspired by the work of Chialvo &
coworkers (Chialvo et al, 1990), while the use of simple linear methods
for stability analysis of ODE systems followed Glass & Mackey (1988).
To understand the performance of Houghton's attentional triad, use was made of :
a. Algebraic determination of steady-state model solutions via setting the rate of change of all variables representing neuronal activities equal to zero.
b. Determination of stability of solutions thus obtained by deriving linear approximations to the system equations and considering their evolution near the steady-states.
c. A simplified map preserving key model properties was derived, thus obtaining a return-map, rather than differential-equation, model.
d.
Graphical analysis of the simplified map was performed to determine the
characteristics of its steady states.
A.
Direct lateral inhibition (DLI) model
This
was used to examine the response of a lumped cortical area under the influence
of an external signal, usually inhibitory, when an appropriate stimulus
is presented. This mimics the response of Auditory areas to a brief tone,
on a background of the constant interference due to Visual (tracking) processing.
It was found that the baseline state of the secondary task unit was typically
reduced by 50% of its maximum response under single task conditions. The
maximum response itself was reduced by 20% only, while the difference in
the timing of the response was very small and depended on the risetime
of the stimulus (figures given here refer to the following set of parameters:
Ext. stimulus I=0.75; W1=0.5; W+ = 0.3; W- = -0.3; D=0.4; Wi(Vis->Aud)=0.7;
Wi(Aud->Vis)=0.35. The exact percentages depend on the parameters used,
but their comparative relations don't: for example, the effect on the baseline
is always stronger than that on maximum activation).
As
the onset of response to the Auditory signal was little affected by the
presence of an inhibiting, concurrent task, it was decided, following Cohen
et al (1992), to take the cumulative activation of a pathway rather than
its risetime behaviour to correspond to behavioural reaction time.
Model
behaviour on stimulus offset was anomalous, units sometimes remaining 'switched
on' after stimulus offset. This difficulty was also encountered in the
much more complicated Houghton models of attention, as described in the
introduction. Here further use of the model was limited to parameter ranges
where stimulus offset is accompanied by decay of cortical activation. The
matter is investigated analytically as follows.
Following
Houghton, we consider the limit where the contribution of inhibitory gain
units is negligible, for example because some external cause has switched
that unit off (the attending dyad approximation). The equations of the
system are :
+
da /dt = -D*a + ( 1 - a ) * W * a } Rln. 2a
p p p on }
da /dt = -D*a + ( 1 - a ) * W1 * [ a ] } Rln. 2b
on on on p
where
a
is a variable describing the state of each unit, D
the time decay constant of the units and Wx
is the weight of synapse x (as in fig. 4a).
The index 'on' refers to the excitatory gain unit, while 'p' to the perceptual
unit. [x] = 0 if x <= 0, while [x] = x
if x > 0. For the equilibrium state, setting both Relns. 1 = 0 gives :
+ 2
W1 * W - D
a = ----------------
p +
W1 * ( D + W ) } Rln. 3
+ 2
W1 * W - D > 0, } Rln. 4
i.e.
the synaptic weights around the positive feedback loop dominate over the
decay constant. This explains why the units of the Houghton model, arranged
in 'attentional triads', can get stuck in an activated state. It can be
further proven that an attentional triad with inputs to each unit has single
unique equilibrium solution if inhibitory drive is adequate to drive the
activation of the perception unit negative and is potentially multistable
for positive driving.
A
simplified return map model was constructed for the attending dyad (or
triad) by observing that for da/dt = 0, each unit in the triad will have
an activation governed by its steady-state input-output curve,
a
= I / (I + D) , } Rln. 5
where
I is any positive input. This curve is convex upwards. The construction
of the map (fig. 7) confirms the analytical results of the ODE model and
suggests that they can be overcome by the adoption of activation curves
which at the point of zero input are concave upwards (fig. 8).
A
given increase in the drive of an off-gain unit always has a larger impact
on reducing perceptual unit activation than an equal increase in the drive
of an on-gain unit, as indeed one might expect by inspection of the general
state equation for a unit:
+ -
da/dt = -D*a + ( 1 - a ) * I - (1+a)* I } Rln. 6
In
summary, the simplest models reveal parameter ranges that have to be avoided
in more complex simulations. The DLI model is however sufficient to demonstrate
how a very simple mechanism could explain cross-modality interference.
B.
Descending attentional control (DAC)
This
is shown in fig. 4b. Attention is mediated by modality specific top-down
activation. This increases response in its own, 'attended', modality and
reduces cross-modality activation. Thus this is the minimal model of the
effect of attention in suppressing non-attended modalities. Top-down activation
or inhibition again affects baseline activation more than response plateau.
In an experiment, for example, where the visual task is ongoing, but the
subject concentrates on detecting the auditory tone when this is to arrive,
the baseline visual state is suppressed by about 80% of the maximum it
then attained when the visual task was under way (from 0 to -.35 in arbitrary
activation units; visual task response was +0.42 ). The auditory baseline
activation increases instead by 38% of its task activation level (from
0 to +0.22; auditory task response +0.58). The precise figures depend on
the parameters used, but if chosen not to fulfil Rln. 4 - i.e. not to have
any spurious stable states - the pattern of results remains robust. Note
that this includes a greater activation, in absolute terms, for the attended
modality compared to the non-attended one, but a lesser activation (lesser
gain) if the baseline state is taken into account (fig. 9).
Thus
the DAC model simulates the normal pattern of cross-modality interference,
and has some success in simulating the qualitative pattern of increased
performance for an attended modality. However, as there are no ascending
connections the cross-modality effects are independent of stimulus processing
and thus the model does not simulate the Baddeley experiments.
C.
Descending and ascending 'match' attentional system (MAS)
This
model (fig 4c) affects lateral inhibition indirectly, and such inhibition
is shown to be dependent both on descending (attentional) activation of
the cross-modality perceptual unit and on the presence of the cross-modality
stimulus. This is the minimal model that simulates features of the dual-task
performance in normal people.
The basic function of the model is shown in fig. 10. In the first part of this figure (a) both stimuli of the dual task are presented, but only one is attended. This is achieved by setting the 'target activation' of the auditory but not the visual pathways to a high value. It is evident that attention changes baseline activation even in the absence of stimuli (cf. behaviour near time = 50). Successful properties of the system include first, peak activation of the attended system being higher than of the non-attended one (0.51 vs. 0.48 in this example: a small difference); and second, a cross-modality effect evident by a drop in visual activation by about 15% of its peak value during the time when both stimuli are present. Fig. 11b shows model behaviour under true dual task conditions, i.e. when attentional activation is present for both modalities. As it should, the model shows greater activation of the visual modality than in fig 10a, and lesser activation of the auditory modality. If we take cumulative activation to correspond to performance, the reduction in area-under-the-curve between the two figures (shaded) is 17%, consistent with experiment in order of magnitude terms. No attempt was made here to simulate the fact that the primary task affects the secondary more than vice versa in most of Baddeley's experiments - this would be straightforward.
2.
Direct lateral inhibition plus desceding/ascending match model
The simplest alteration of the basic MAS model that will provide a robust effect of increasing the dual task effect when the efficiency of the matching mechanism is impaired is to combine the DLI and MAS systems. Simulation of this system allows quantitative match with the Baddeley experiments over a large range of parameter values.
3.
Revised model - preliminary results
A.
Alternative neural unit equations
Simulations
were performed while substituting the Grossberg equations on which the
original Houghton model is based with the Freeman 'KI' equations. The DAC
model was implemented using the KI equations, and it was confirmed by simulation
that activation of the on-gain unit by positive attentional input here
not only increases activation on presentation of a stimulus, but also the
difference between baseline and response activations, thus truly increasing
the gain of the triad. A second result is although high synaptic weight
products around the positive feedback loop can still result in spuriously
activated states, the system is not as sensitive to this effect, again
as predicted by graphical analysis (fig 8). Secondly, activation of attentional
triads made of KI units can easily be made to oscillate, consistent with
physiological studies. Finally, KI units respond much more slowly to step
inputs than the original Grossberg units. They may thus lend themselves
more readily to investigation of time-dependent phenomena such as reaction
times.
As
an example, the set of W1 = W+ = - W- = 0.7, T1 = 0.2 T2 = 0 produces a
baseline activation of 0.17, increasing to 0.83 on stimulus presentation.
When T1 = T2 = 0, baseline activation is 0 and response to stimulus 0.5.
In this example the increase in gain is about 30%.
B.
Alternative matching mechanism
Here the criterion for a successful 'match' is the synchronised activation of `perceptual / object field' and `association / match' areas, rather than just the level of activation of these areas. In the normal case the models shows synchronisation dependent on first, the presence of a stimulus and second, the presence of descending ('target') activation (data not shown). In the case of dual task synchronisation is again achieved, but not as smoothly (fig. 14); for high levels of interference the speed with which it is achieved varies with the precise characteristics of the interfering noise within each trial. Also achieved is the establishment of a cooperative oscillation with increase in the overall level of activity of the cooperating areas much as in the models above.
In the case of AD and in the absence of interfering activity (single task condition), synchronisation appears to be achieved more slowly and the cooperative activity has lesser magnitude, consistent with a decreased performance (fig. 15). This was suggested by preliminary exploration of the model. The introduction of interference (dual task) in the case of AD can disrupt synchronisation gravely or even abolish it completely (consistent with 'missed responses' in Baddeley's experiments) . This model therefore appears to have the potential to simulate the progression of AD from a prolongation of reaction times to complete task failure. Full exploration of this promising model has been carried out. Please contact me for details
Figure
15: Simulation of AD during single task performance, showing a. the
response of the association area and b. synchronisation (cf. fig 14b &
d). AD is simulated by a reduction in the inter-area coupling Kxx (cf.
fig 6c). A cooperative oscillation is again produced, but is of smaller
amplitude. There is no interfering modality, so the system smoothly but
slowly converges to a near-zero phase value.
This study
is novel in bringing together rigorous neuropsychology, current theories
of attentional control and computer modelling to bear upon the study of
Alzheimer's disease. Although models of dysmnesia have been developed in
AD, models of executive function have not. Rigorous neuropsychological
experiments are seldom used as a basis for modelling in AD.
Even more
novel and important is the use of dynamical systems theory to bridge the
informational and biological domains. This is crucial as neural activity
shows complex patterns, including oscillations, whose emergence is intimately
linked to the brain's information processing ability. On the other hand,
even simple psychological models have important, unexplored dynamical properties.
The present
approach does not apply simply the minimal modifications that would fix
model shortcomings, without reference to the biological substrate. Lack
of such reference can be likened to trying to understand how a hummingbird
flies by building helicopters: both might be able to hover, but the biological
implementation may be fundamentally different to the engineering one. Models
are better developed by examining hitherto neglected biological data while
recognising that the relevant biology may be as yet unknown. Another objection
to the 'minimal modification' approach is that it is Kuhn's 'normal science'
par excellence (Notturno 1984), designed to avoid fundamental issues.
'Executive
dysfunction' in the dual task may not have the same origin or importance
as the impairment of ADL related functions. Hence it may not be the most
relevant starting point. The underlying attentional theory of Houghton
et al is not the only possible starting point for modelling either. The
information processing theory of EPIC (Meyer & Kieras, 1997) could
be a different one; The methodology of Cohen et al (1992) yet another,
particularly as a lot of work has examined the Stroop task in AD. The EPIC
model was not used as it has very coarse graining of the biological processes
involved. It involves however a detailed consideration of cognitive strategy,
which has been neglected in our study. The Baddeley experiments did not
consider differences in cognitive strategy and thus would be difficult
to reconcile with EPIC. It would not be surprising if AD patients had difficulties
inventing efficient strategies, and these would have to be considered in
the modelling of more complex tasks. This is relevant to the conjecture
that better strategies help the more educated preserve function in the
face of AD.
Baddeley's
papers provide little detail into the particular ways in which performance
deteriorated under different conditions. They do not, for example, report
false-positive responses or evidence of strategy failure. Such information
could lead to more detailed modelling. Access to the original data might
help. In addition the conclusion of an 'Executive lesion' in AD reached
by Baddeley and assumed in this study is not the only possible one. An
alternative could be deficient automatization. Rather than AD impairing
executive control it could impair the process of automatisation (Cohen
et al, 1992), so that AD subjects find dual tasks harder because it is
harder to combine non-automatised tasks.
Modelling
studies make many assumptions, which can be both a weakness and a strength.
Unproved assumptions used in the Houghton derived models which could reduce
the validity of our conclusions include that: 1. Performance is directly
related to level of activation of cortical areas 2. The most relevant cortical
areas are perceptual; 3. Diffuse influences (e.g. ACh) can be neglected;
4. Cortices can be treated in a 'lumped' manner; 5. Neural noise can be
neglected; 6. The way in which 'match' unit performance is impaired is
immaterial; 7. Cross-modality interference can be assumed to take place
via descending projections of the attentional mechanism 8. Parameter exploration
was adequate. The related strength of the approach is that such assumptions
are inherent in much thinking about attentional control, only they remain
hidden. On the other hand in this study they serve as incentives for meaningful
exploration. The manner in which some of these assumptions were dealt with
has been described in Methods. With respect to assumption 2., simulation
of variables more directly related to behavioural performance would be
highly desirable. Assumption 7 is of particular concern. It is adopted
on the grounds of consistency with imaging studies showing suppression
of non-attended modalities, of it providing a task-specific mechanism of
attentional control and of it being a priori unbiased with respect to the
study hypothesis. These assumptions will be discussed further.
A. Models
directly based on Houghton's theory
The 'attentional
triad' proved a useful model. It is susceptible to attentional modulation,
easily allowing the implementation of excitatory and inhibitory attentional
control, including lateral inhibition and the match mechanism. It was,
however, found to have important shortcomings. First, it can show spuriously
excited states. Second, it is a lot more responsive to inhibitory rather
than excitatory modulation. Third, the 'gain' units alter the level of
activation but do not, in fact, change gain itself. These problems were
traced (fig. 7) to the steady-state input-output function of the specific
units involved. This curve has its maximum sensitivity at the resting state.
The use of excitation functions which are concave upwards at the origin
can solve these problems (fig. 8; section 3A of Results). This is an illustration
of why knowledge of dynamical properties of simple unit combinations can
be important.
In addition,
the triad involves no conduction delays. More realistic, KI-like units
improve the points raised above, have physiological support and also address
the issue of time dependence in a more substantial way. They provide a
relatively well-explored avenue to the simulation of oscillatory cortical
dynamics. Objections can be raised: The KI equations have been derived
from archicortical rather than neocortical physiology; and the timescales
on which KI units operate do not relate to behavioural reaction times (their
response is still too fast). Therefore an indirect index of their response
would still have to be used to infer behavioural performance.
The limitations
of the 'attentional triad' caused problems in dual-task simulations. If
cross-modality inhibition is used, then performance of each task has to
overcome inhibition by the other. This is difficult to achieve if excitatory
control is inherently weaker than inhibitory. It can result in attention
paradoxically decreasing performance during the dual task and in damage
to the attentional system dis-inhibiting perceptual units more than de-exciting
them. The result is a tendency for the AD condition to perform relatively
better in the dual task than normals, except within a limited parameter
range. This includes: 1. low stimulus intensities, which essentially linearise
perceptual unit response; 2. within-pathway, excitatory synapses from match
units to on-gain units much (e.g. four times) stronger than the cross-modality,
inhibitory ones. It can be seen that both these properties serve to offset
the tendency of the original ('Grossberg') units to saturate and to favour
inhibitory inputs. These considerations also explain why even successful
models showed quantitatively small effects.
Another
drawback of the initial models is that progressive impairment of the 'match'
mechanism does not lead to the profound effects of advancing AD; rather,
impairment shows a floor effect. This can be understood if the positive
feedback loop formed by the perceptual-match connections is considered.
The participation of Grossberg-type units necessitates a low gain around
the loop to avoid spurious states. An improved high-gain loop (as in fig.
8) would be more suitable for the simulation of the profoundly deleterious
effect of AD.
On the other
hand, the Houghton-based model with suitable qualifications succeeded in
simulating qualitatively the overall pattern of the Baddeley results. It
seems that the essential features of this model include: 1. the matching
process 2. the presence of an active description of behavioural targets
and 3. change of responsiveness of perceptual units towards stimuli through
both inhibitory (decreasing responsiveness) and excitatory (increasing
it) attentional control. Shortcomings of the original models include use
of the Grossberg units and the symbolic implementation of the match / mismatch
mechanism. Features that do not keep with cortical physiology include the
zero transmission delays and system operation via point attractors.
B. The
success of lateral inhibition : Interpretation and novel predictions of
the augmented model.
The combination
of direct lateral inhibition and match-based attentional control provided
a strikingly robust model, simulating a number of results in a quantitative
manner. This is because it involves excitatory attentional control based
at the level of the match mechanism, with cross-modality inhibition at
a lower level, less prone to the ravages of AD. This model leads to some
novel predictions that are physiologically and psychologically testable.
Cross-modality inhibition is independent of attention for a given sensory
cortical activation. As in the Baddeley experiments it takes place between
procedurally unrelated tasks it is likely to operate in a general, non-specific
manner: different modalities laterally inhibit each other by default. Such
laterally inhibitory effects would be equivalent in AD and normals as long
as subjects attended to neither of the interfering modalities, performing
instead some third, neutral task. Such effects should be visible in human
imaging activation studies as well as in invasive animal experiments.
What could
be the biological function of cross-modality interference effects observed
in the normal elderly, and more specifically of lateral inhibition? Cohen
and co-workers reach the conclusion in their discussion of automatic task
performance that tasks should interfere only to the extent that they utilise
overlapping brain modules, as opposed to some ill defined 'attentional
resource'. Similar principles underlie the dual task performance limitations
of the conceptually different EPIC (Meyer & Kieran, 1997). However,
Baddeley's experiments specifically minimise overlap between resources
required to perform the constituent tasks. Only then does the finding of
interference between the tasks, particularly between response-to-tones
and tracking, lend support to the theory of a 'central executive'. This
is considered as a shared resource, the homuncular manager of a limited
attentional 'budget'. There is, however, poor support for such a shared
resource.
Cross-interference,
so far modelled by direct lateral inhibition, could happen for a number
of reasons. First, the 'central executive' could be a brain processor which
can only deal in terms of serial procedures. The interference between increasingly
more demanding tasks would arise because the only way of multitasking this
serial processor is timesharing - and high performance biological serial
computation has not yet evolved. This is broadly consistent with the analysis
of H.A. Simon (1995), who claims that human thought is largely serial because
parallel processing becomes inefficient in a general purpose reasoning
device. Second, it may be that the organism doesn't know a priori that
two tasks won't clash. The default is mutual inhibition, as per the direct
lateral inhibition paradigm. Still, this isn't a good explanation for an
inhibitory setup. The cross-inhibitory 'setup' might be an example of a
synergy phenomenon. Synergies are ensembles of large numbers of biological
components (e.g. muscles, sensory organs, nerves, central circuits) which,
during a task, are coordinated and behave as a whole. As these are all
functionally bound together they lack freedom to behave relative to each
other except as prescribed by the pattern of coordination (Bernstein 1967,
as quoted in Kelso, 1995; Kelso et al, 1984). It may be that the ability
for coordination is a ubiquitous characteristic of behaving organisms.
This ability is achieved through couplings between all system levels and
components. In the presence of such couplings random relative component
activity (as is forced by the random relative timing of the two tasks in
the dual tasks paradigm) runs contrary to the general propensity for coordination.
Contrary to the dual task paradigm, organisms are good at multi-task performance
(e.g. walking, breathing and talking) if constraints of random relative
timing are lifted. This may be important in AD, where walking while talking
becomes difficult. Failure in this routine dual task may contribute to
some dangerous falls (Camicioli et al, 1997). In fact many behaviours can
be considered to involve multiple tasks having arbitrary but non-random
relations to each other. Cross-modality inhibition could be not necessarily
inhibition per se, but a vestige of cross-modality propensity for coordination.
Again, however, impaired dual task performance is just a side effect.
Finally
the lateral inhibition may have another function. In normal environments
animals utilise multi-sensory inputs. It would be disastrous for mice to
be impaired from hearing cats simply by looking for them. On the contrary,
cross-modality control could highlight in the 'interfered' modality features
of survival value in a context defined by the 'primary' modality, while
inhibiting known distractors. Irrelevant cross-modality stimuli would be
neither highlighted nor inhibited.
Thus several
questions arise: What dual tasks show cooperative, and what neutral, effects
in normals ? and what happens in these tasks in AD ?
It is important to run the whole series of simulations in this study, and particularly the MAS ones, by substituting units most compatible with cortical physiology for the Grossberg ones. These in the first instance should be KI sets. It is also important to simulate output processes including the 'binding' between perception and response selection. This would allow direct comparisons with experimental results such as reaction times and recall errors. It may be possible to simulate the digit-span experiments of Baddeley by using the successful models of Burgess & Hitch (1996) for short term memory for serial order (fig. 17).
5.
Reflections in the light of other findings
As the discussion of the possible roles and biological substrates for cross-modality inhibition has shown, not only is it important to consider model development but also fundamental assumptions, while retaining successful general features of the present theory. This can be done by considering what ways of implementing these successful features would be most compatible with the current understanding of the neurophysiology of AD. Successful matching in the Houghton models involves an increase in activation of both the 'match / mismatch' and the perceptual neurons. While both sensory and frontal cortices are thought to be activated by attention and by concurrent tasks, it is not clear how the pattern of activation changes in AD.
Some important studies (Leuchter et al, 1992; Schreiter-Gasser et al, 1993) largely gave rise to the hypothesis that intercortical functional connectivity is especially impaired in AD and that it may account for 'dysexecutive' neuropsychological deficits. These studies influenced our modelling of AD as reducing the influence of the 'match' on the perceptual units. These studies however measured levels of EEG synchronisation of cortical oscillations, rather than levels of activation, between different cortical areas. It may therefore be that the matching process depends on such oscillatory processing.
Three further
lines of evidence, support this alternative view of matching. First, animal
data suggest that neural structures closer to the sensory receptors receive
bias, thus favouring some responses over others, through descending oscillatory
signals (Kay et al, 1996; Sillito et al, 1994). This is in addition to
the oscillatory signals of primary sensory cortex found during percept
recognition (Gray, 1994). Second, during visuomotor tasks human EEG shows
that successful trials are accompanied by increased synchronisation between
different but relevant brain areas. Gevins & Cutillo (1995) describe
increased evoked potential covariance between left prefrontal cortex and
the relevant motor and parietal cortices preceding successful responses.
Third, Bressler and coworkers (1993) showed how attention-demanding tasks
involve intermittent synchronisation of oscillatory activities among multiple
brain areas in the monkey. Synchronisation takes place very robustly, and
in discrete time frames, over cortical areas of obvious relevance. However
the findings are difficult to interpret because synchronisation epochs
do not map to information processing stages in an obvious way (unlike the
work of Kay et al in the olfactory system). Synchronisation may indicate
'consensual resolution of processing', whereby synchronous activity in
two or more areas allows amplification of the signal transmitted to common
targets that they project to (Bressler, 1995).
Thus matching
may involve synchronisation and AD could disrupt it through functional
disconnection, as demonstrated by the EEG studies. This could explain attentional
impairment in a general way. Cross-modality interference need not become
attenuated: Leuchter and coworkers (1992) clearly showed that coherence
of oscillatory activity between areas connected by broad, complex networks
is preserved. Most interestingly, imaging studies (Becker et al, 1996)
have shown that AD subjects activate cortical areas according to task demands
more diffusely than controls, activation 'spilling' into neighbouring areas.
Synaptic compensatory changes that account for the excess of false-positive
events during memory tasks in AD (Ruppin & Reggia, 1995) could also
account for this 'spill-over' effect. With respect to dual task performance,
such 'spill-over' would be consistent with a preserved or enhanced cross-task
interference.
In summary,
a more satisfactory model of cross-modality interference could include
the physiological feature of oscillatory synchronisation and, in AD, an
impairment of the balance between long-range functional connectivity and
more local interference. This is in addition to the target units, the matching
mechanism based on recurrent connections and the perceptual mechanism modulated
by attention central to the successful psychological models.
6.
New directions for further research
A. Structure
of a revised model
The overall
structure of the models used in this study (fig. 3) can be retained in
incorporating the above improvements. However, the cross-modality inhibitory
attentional projections are now omitted and local (between areas of the
same level) interference is postulated. The 'match' units are again identified
with association cortices. Each lumped cortical area is now capable of
oscillatory activity. Activation of motor schemata is through 'consensual
resolution of processing'. A diagram of the revised model is shown in fig
6a
The pattern
of the Baddeley results permits however drastic simplifications to be made.
In both all subjects the influence of the primary on the secondary task
was much greater than vice versa. We can therefore ignore the latter influence.
Also ignoring motor schema binding gives a preliminary model of only two
coupled oscillators. This bare bones model is shown in fig.
6b. Here the influence of the primary task is through a non-specific
broad band signal.
B. Evaluation
of the revised model
Simulations
showed that the model is successful in simulating the overall pattern of
experimental results. Attentional activation improves its performance (indeed
for certain ranges of parametres attention is necessary for model response).
Simulation of AD by functional dysconnection of modules gradually impairs
performance, disproportionately so in the presence of an interfering task
(This is a summary of preliminary results. Please
email me for fuller info). In the normal case the presence of
interference does not greatly reduce performance.
If
information processing in the real brain involves a series of intermittent
synchronisations like the one successfully simulated here, the demonstrated
delays in synchronisation might be possible to directly link with prolongation
of reaction time. The problem here is that the broad band nature of cortical
oscillations does not clearly reveal a timescale with which the period
of oscillation of the simulated areas can be identified.
The revised
model is still inadequate in simulating physiology. Possible important
features that are omitted are the explicitly episodic nature of brain synchronisation;
the detailed description of the oscillators used; and most importantly,
all structure of information processing that the real episodic synchronisations
reflect. Another omitted feature is the non-negligible transmission delays
of real intercortical pathways. The model however captures enough of the
physiology to support the hypothesis in a preliminary manner.
C. Education
& Alzheimer's disease
Many aspects
of AD related to executive function particularly lend themselves to modelling.
A most important example has to do with the possible effect of lack of
education, an independent risk factor for the development of AD (Orrell
& Sahakian, 1995). Functional imaging indicates greater deterioration
for a given degree of cognitive impairment in the brain areas subserving
attentional and executive functions of the better educated (Alexander et
al, 1997). This supports the hypothesis that better education is associated
with greater 'cognitive reserve'. The biological and psychological substrates
of the protective effect of education are however little understood and
could in the future be explored according to our paradigm. This might be
of preventative value.
In Alzheimer's
dementia research has recently explored not only the memory but also the
so called 'dysexecutive' deficits (Baddeley et al, 1991). The latter are
thought to reflect difficulties in the coordination of daily activities
that compromise the independence of patients with AD at least as much as
memory deficits. The understanding of these executive deficits is thus
of great importance. Baddeley and co-workers (1991) postulated a Central
Executive System (CES) coordinating attention. Using the dual-task paradigm
they showed that the CES is particularly affected in DAT, accounting for
the severe attentional /executive deficits (Baddeley et al, 1991). These
experiments provided the necessary data to constrain the development of
rigorous models of executive control of attention.
Houghton
and coworkers (Houghton & Tipper, 1994) have simulated successfully
many aspects of selective allocation of attention. We formulated the hypothesis
that "an application of the model of attentional control of Houghton et
al can account for the pattern of deficit observed by Baddeley in AD patients
during the performance of dual tasks". We aimed further to improve the
model heuristically but also on the basis of current neuroscientific findings.
Two attentional
control systems, one for each dual task modality, were combined (fig. 3).
The two systems were linked by cross modality inhibitory attentional control
(Houghton & Tipper, 1994). Damage due to AD was modelled as an impairment
of the influence of the attentional ('match') module on perceptual areas,
guided by the neuropathological finding that in early AD association areas
and corresponding long range projections are damaged (Morris, 1994).
It was found
that within a small parameter range the model could reproduce qualitatively
the general pattern of experimental results. However it had a tendency
to show spuriously activated states, lacked robustness, had difficulty
reproducing results in a quantitative way and failed to simulate the progressive
deterioration of executive function in AD. Dynamical analysis showed that
the units used to simulate individual cortical areas in the original models
were largely to blame for these failures. The model could be dramatically
improved by the introduction of lateral inhibition between sensory areas,
postulated to be largely unaffected by AD. Preliminary studies showed than
association function based on synchronisation of oscillations between different
brain areas, known to deteriorate in AD, could also account for Baddeley's
results.
The study
has contributed to the understanding of psychological mechanisms of executive
function particularly as involved in AD. It has demonstrated the importance
of dynamical understanding of psychological models. It brought together
rigorous neuropsychology and computer modelling, something not previously
attempted in this field but essential for the linking of the pathology
with the clinical features of AD. It has also indicated directions for
future research, mainly the investigation of the 'matching' function in
the context of oscillatory cortical dynamics and the disruption of intercortical
coordination of neuroactivity in AD.
This thesis would have been impossible without the help of Drs. Martin Orrell and George Houghton. I would further like to thank Dr. Ann Moutoussi and Dr. David Frost for precious glimpses into their world-views, and the staff of the Medical Education Centre, Whipp's Cross Hospital, Leytonstone for their help with literature searching and provision of reference papers.
Email me for a gzip - compressed
version of these samples
Example
of literature search strategy showing progression from wider to more specific
terms. This search was undertaken with librarian guidance.
Database: Medline <1996 to June 1999>
Set Search Results
----------------------------------------------------------------------
001 alzheimer disease/ 5941
002 alzheimer's.tw. 5926
003 alzheimer's disease.tw. 5519
004 alzheimers.tw. 5926
005 1 or 2 or 3 or 4 7555
006 dementia/ 2552
007 dementia.tw. 4541
008 6 or 7 5266
009 5 or 8 10618
010 executive function.tw. 148
011 central executive.tw. 40
012 supervisory attentional system.tw. 3
013 supervisory attentional.tw. 4
014 attentional control.tw. 29
015 control of attention.tw. 11
016 attention/ 3502
017 attention.tw. 13531
018 dual task.tw. 95
019 dual task experiment.tw. 1
020 concurent task.tw. 0
021 concurrent task.tw. 13
022 simultaneous task.tw. 3
023 dual task$.tw. 101
024 simultaneous task$.tw. 3
025 concurrent task$.tw. 27
026 10 or 11 or 12 or 13 or 14 or 15 or 16 or 17 15808
027 stroop.tw. 234
028 18 or 19 or 20 or 21 or 22 or 23 or 24 or 25 or 27 352
029 computer simulation/ 6221
030 model.tw. 75696
031 model$.tw. 96966
032 connectionist model$.tw. 37
033 "neural networks (computer)"/ 1395
034 neural network$.tw. 1412
035 dynamical model.tw. 30
036 dynamical model$.tw. 34
037 29 or 32 or 33 or 34 or 35 or 36 7900
038 9 and 26 and 28 and 37 1
039 28 and 37 7
040 9 and 26 325
041 9 and 28 10
042 from 39 keep 1-3,6 4
043 26 and 37 164
044 43 and 9 4
045 from 41 keep 3-8 6
046
45 or 42 10
Contents Abstract
Introduction Aims
Methods Results
Discussion
Acknowledgements
References
Appendices